---------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /usr/local/stata/projects/Chicago votes experiment/do and log files/analysis-chicago-experim
> ent-4.log
  log type:  text
 opened on:  18 May 2017, 12:47:16

. 
. /* Peter Miller */
. /* April 24, 2016 */
. 
. use "multilevel chicago experiment data-3rd version"

. 
. gen log_pop=log(total_pop)

. replace med_hh_inc=med_hh_inc/1000
med_hh_inc was long now double
(52324 real changes made)

. 
. gen white=race

. recode white 2/4=0
(white: 26667 changes made)

. 
. gen black=race

. recode black 1=0 2=1 3/4=0
(black: 52324 changes made)

. 
. gen latino=race

. recode latino 1/2=0 3=1 4=0
(latino: 52324 changes made)

. 
. gen other=race

. recode other 1/3=0 4=1
(other: 52324 changes made)

. 
. * Descriptive statistics of the data
. 
. tab white,missing

      white |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     26,667       50.97       50.97
          1 |     25,657       49.03      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab black,missing

      black |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     39,432       75.36       75.36
          1 |     12,892       24.64      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab latino,missing

     latino |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     45,141       86.27       86.27
          1 |      7,183       13.73      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab other,missing

      other |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     45,732       87.40       87.40
          1 |      6,592       12.60      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab age,missing

        age |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |      1,068        2.04        2.04
         19 |      2,203        4.21        6.25
         20 |      2,570        4.91       11.16
         21 |      2,956        5.65       16.81
         22 |      3,054        5.84       22.65
         23 |      3,312        6.33       28.98
         24 |      4,072        7.78       36.76
         25 |      4,484        8.57       45.33
         26 |      5,136        9.82       55.15
         27 |      5,408       10.34       65.48
         28 |      5,593       10.69       76.17
         29 |      5,977       11.42       87.59
         30 |      6,491       12.41      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab race,missing

       race |      Freq.     Percent        Cum.
------------+-----------------------------------
      White |     25,657       49.03       49.03
      Black |     12,892       24.64       73.67
     Latino |      7,183       13.73       87.40
      Other |      6,592       12.60      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab treatment,missing

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     30,145       57.61       57.61
          1 |     22,179       42.39      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab vote,missing

       vote |      Freq.     Percent        Cum.
------------+-----------------------------------
         -1 |     34,999       66.89       66.89
          0 |     14,459       27.63       94.52
          1 |      2,064        3.94       98.47
          2 |        802        1.53      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab vote2,missing

      vote2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     34,999       66.89       66.89
          1 |     17,325       33.11      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab febmunicipal11,missing

febmunicipa |
        l11 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     15,572       29.76       29.76
          . |     36,752       70.24      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. tab general14,missing

  general14 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     34,953       66.80       66.80
          . |     17,371       33.20      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. 
. xtile quart_inc=med_hh_inc,n(4)

. 
. gen edu=pct_college_grad+pct_pro_degree

. xtile quart_college=edu,n(4)

. 
. tab quart_inc

4 quantiles |
         of |
 med_hh_inc |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |     13,090       25.02       25.02
          2 |     13,094       25.02       50.04
          3 |     13,078       24.99       75.04
          4 |     13,062       24.96      100.00
------------+-----------------------------------
      Total |     52,324      100.00

. 
. bysort quart_inc: tab treatment

---------------------------------------------------------------------------------------------------------
-> quart_inc = 1

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,558       57.74       57.74
          1 |      5,532       42.26      100.00
------------+-----------------------------------
      Total |     13,090      100.00

---------------------------------------------------------------------------------------------------------
-> quart_inc = 2

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,491       57.21       57.21
          1 |      5,603       42.79      100.00
------------+-----------------------------------
      Total |     13,094      100.00

---------------------------------------------------------------------------------------------------------
-> quart_inc = 3

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,508       57.41       57.41
          1 |      5,570       42.59      100.00
------------+-----------------------------------
      Total |     13,078      100.00

---------------------------------------------------------------------------------------------------------
-> quart_inc = 4

  treatment |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,588       58.09       58.09
          1 |      5,474       41.91      100.00
------------+-----------------------------------
      Total |     13,062      100.00


. 
. sum total_pop log_pop med_age pct_white pct_black pct_other pct_latino pct_less_hs pct_hs_diploma pct_s
> ome_college pct_college_grad pct_pro_degree med_hh_inc

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
   total_pop |     52324    4088.954    2039.372        178      16375
     log_pop |     52324    8.199516     .494851   5.181784   9.703511
     med_age |     52324    34.07959    5.800113       15.4       59.6
   pct_white |     52324    47.26024    34.50218          0       98.4
   pct_black |     52324    37.14585    40.67016          0        100
-------------+--------------------------------------------------------
   pct_other |     52324    15.59391    15.05694          0       90.9
  pct_latino |     52324    21.04737    26.34246          0       98.7
 pct_less_hs |     52324    16.61179    13.12115          0       70.9
pct_hs_dip~a |     52324    21.82888     12.0875          0       58.6
pct_some_c~e |     52324    23.81922    10.09553        3.5       52.2
-------------+--------------------------------------------------------
pct_colleg~d |     52324    22.24098    15.30569          0       64.8
pct_pro_de~e |     52324    15.49766    13.60169          0       64.3
  med_hh_inc |     52324    30.47682    15.74349        2.5      80.25

. 
. corr total_pop med_age pct_white pct_black pct_other pct_latino pct_less_hs pct_hs_diploma pct_some_col
> lege pct_college_grad pct_pro_degree med_hh_inc
(obs=52324)

             | total_~p  med_age pct_wh~e pct_bl~k pct_ot~r pct_la~o pct_le~s pct_hs~a pct_so~e pct_co~d
-------------+------------------------------------------------------------------------------------------
   total_pop |   1.0000
     med_age |   0.0789   1.0000
   pct_white |   0.1071  -0.0696   1.0000
   pct_black |  -0.1866   0.1375  -0.9328   1.0000
   pct_other |   0.2585  -0.2118   0.2281  -0.5637   1.0000
  pct_latino |   0.1285  -0.2842   0.2657  -0.4895   0.7134   1.0000
 pct_less_hs |   0.0158  -0.2774  -0.3642   0.1546   0.4169   0.6568   1.0000
pct_hs_dip~a |  -0.0474   0.0066  -0.5836   0.5024  -0.0198   0.2473   0.5958   1.0000
pct_some_c~e |  -0.0950   0.3423  -0.6929   0.7111  -0.3331  -0.2447   0.1056   0.5363   1.0000
pct_colleg~d |   0.0182  -0.0274   0.7035  -0.5737  -0.0622  -0.2882  -0.7409  -0.8751  -0.6340   1.0000
pct_pro_de~e |   0.0774   0.0384   0.5928  -0.4780  -0.0673  -0.3471  -0.7386  -0.8768  -0.6074   0.8377
  med_hh_inc |   0.1693   0.1111   0.6611  -0.5392  -0.0584  -0.2395  -0.6808  -0.7585  -0.5677   0.8351

             | pct_pr~e med_hh~c
-------------+------------------
pct_pro_de~e |   1.0000
  med_hh_inc |   0.8126   1.0000


. 
. * Run statistical power analyses
. * First estimate minimum detectable sample given turnout means and 95% power
. 
. power onemean .327 .336, sd(.47) power(.95)

Performing iteration ...

Estimated sample size for a one-sample mean test
t test
Ho: m = m0  versus  Ha: m != m0

Study parameters:

        alpha =    0.0500
        power =    0.9500
        delta =    0.0191
           m0 =    0.3270
           ma =    0.3360
           sd =    0.4700

Estimated sample size:

            N =     35441

. 
. * Second estimate statistical power of our data
. 
. power onemean .327 .336, sd(.47) n(52324)

Estimated power for a one-sample mean test
t test
Ho: m = m0  versus  Ha: m != m0

Study parameters:

        alpha =    0.0500
            N =     52324
        delta =    0.0191
           m0 =    0.3270
           ma =    0.3360
           sd =    0.4700

Estimated power:

        power =    0.9922

. 
. * Create balance table
. 
. hotelling vote2 female age white black latino log_pop med_age pct_white pct_black pct_latino pct_hs_dip
> loma pct_some_college pct_college_grad pct_pro_degree med_hh_inc, by(treatment)

---------------------------------------------------------------------------------------------------------
-> treatment = 0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |     30145    .3271521    .4691811          0          1
      female |     30145    .5965168    .4906042          0          1
         age |     30145    25.43792    3.411763         18         30
       white |     30145    .4889368    .4998859          0          1
       black |     30145    .2467076    .4311022          0          1
-------------+--------------------------------------------------------
      latino |     30145    .1381987    .3451141          0          1
     log_pop |     30145    8.202437    .4952841   5.181784   9.703511
     med_age |     30145    34.08805     5.79516       15.4       59.6
   pct_white |     30145    47.26514    34.51675          0       98.4
   pct_black |     30145    37.13804    40.64781          0        100
-------------+--------------------------------------------------------
  pct_latino |     30145    21.01514    26.30179          0       98.7
pct_hs_dip~a |     30145    21.85926    12.11545          0       58.6
pct_some_c~e |     30145     23.8064    10.10508        3.5       52.2
pct_colleg~d |     30145    22.22525    15.32793          0       64.8
pct_pro_de~e |     30145    15.50693    13.63083          0       64.3
-------------+--------------------------------------------------------
  med_hh_inc |     30145    30.54451    15.79421        2.5      80.25

---------------------------------------------------------------------------------------------------------
-> treatment = 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |     22179    .3364895    .4725192          0          1
      female |     22179    .5932188    .4912445          0          1
         age |     22179    25.48095    3.431911         18         30
       white |     22179    .4922675    .4999515          0          1
       black |     22179    .2459534    .4306607          0          1
-------------+--------------------------------------------------------
      latino |     22179    .1360296    .3428277          0          1
     log_pop |     22179    8.195546    .4942453   5.181784   9.703511
     med_age |     22179     34.0681    5.806949       15.4       59.6
   pct_white |     22179    47.25358    34.48315          0       98.4
   pct_black |     22179    37.15646    40.70143          0        100
-------------+--------------------------------------------------------
  pct_latino |     22179    21.09117    26.39815          0       98.7
pct_hs_dip~a |     22179    21.78757    12.04956          0       58.6
pct_some_c~e |     22179    23.83665    10.08274        3.5       52.2
pct_colleg~d |     22179    22.26236    15.27574          0       64.8
pct_pro_de~e |     22179    15.48508    13.56229          0       64.3
-------------+--------------------------------------------------------
  med_hh_inc |     22179    30.38481    15.67417        2.5      80.25


2-group Hotelling's T-squared = 18.248769
F test statistic: ((52324-16-1)/(52324-2)(16)) x 18.248769 = 1.1402211

H0: Vectors of means are equal for the two groups
              F(16,52307) =    1.1402
       Prob > F(16,52307) =    0.3099

. 
. * Estimating treatment effect by a difference of means test
. 
. bysort treatment: tab vote2

---------------------------------------------------------------------------------------------------------
-> treatment = 0

      vote2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     20,283       67.28       67.28
          1 |      9,862       32.72      100.00
------------+-----------------------------------
      Total |     30,145      100.00

---------------------------------------------------------------------------------------------------------
-> treatment = 1

      vote2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     14,716       66.35       66.35
          1 |      7,463       33.65      100.00
------------+-----------------------------------
      Total |     22,179      100.00


. sum vote2 if treatment==0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |     30145    .3271521    .4691811          0          1

. sum vote2 if treatment==1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |     22179    .3364895    .4725192          0          1

. ttesti 30145 .3271521 .4691811 22179 .3364895 .4725192

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       x |   30145    .3271521    .0027023    .4691811    .3218555    .3324487
       y |   22179    .3364895    .0031728    .4725192    .3302705    .3427085
---------+--------------------------------------------------------------------
combined |   52324      .33111    .0020574    .4706171    .3270775    .3351425
---------+--------------------------------------------------------------------
    diff |           -.0093374    .0041632               -.0174972   -.0011776
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  -2.2429
Ho: diff = 0                                     degrees of freedom =    52322

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0125         Pr(|T| > |t|) = 0.0249          Pr(T > t) = 0.9875

. 
. * The difference of means test shows the increase in voting in the treatment group is significantly hig
> her (at the 5% level).
. * than the control group by 0.93 points
. 
. * Estimate turnout rate by treatment group and income quartile
. 
. bysort quart_inc treatment: sum vote2

---------------------------------------------------------------------------------------------------------
-> quart_inc = 1, treatment = 0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      7558    .2676634    .4427704          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 1, treatment = 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      5532    .2651844    .4414712          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 2, treatment = 0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      7491    .3481511     .476416          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 2, treatment = 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      5603    .3453507    .4755249          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 3, treatment = 0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      7508    .3890517    .4875676          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 3, treatment = 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      5570    .4066427    .4912512          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 4, treatment = 0

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      7588     .304428    .4601951          0          1

---------------------------------------------------------------------------------------------------------
-> quart_inc = 4, treatment = 1

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
       vote2 |      5474    .3280965     .469563          0          1


. 
. * difference of means tests
. 
. ttesti 7558 .2676634 .4427704 5532 .2651844 .4414712

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       x |    7558    .2676634     .005093    .4427704    .2576797    .2776471
       y |    5532    .2651844    .0059356    .4414712    .2535484    .2768204
---------+--------------------------------------------------------------------
combined |   13090    .2666157    .0038651    .4422066    .2590397    .2741918
---------+--------------------------------------------------------------------
    diff |             .002479    .0078247               -.0128585    .0178165
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =   0.3168
Ho: diff = 0                                     degrees of freedom =    13088

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6243         Pr(|T| > |t|) = 0.7514          Pr(T > t) = 0.3757

. ttesti 7491 .3481511 .476416 5603 .3453507 .4755249

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       x |    7491    .3481511    .0055045     .476416    .3373608    .3589414
       y |    5603    .3453507    .0063528    .4755249    .3328968    .3578046
---------+--------------------------------------------------------------------
combined |   13094    .3469528    .0041599    .4760187    .3387987    .3551069
---------+--------------------------------------------------------------------
    diff |            .0028004     .008408               -.0136806    .0192814
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =   0.3331
Ho: diff = 0                                     degrees of freedom =    13092

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6305         Pr(|T| > |t|) = 0.7391          Pr(T > t) = 0.3695

. ttesti 7508 .3890517 .4875676 5570 .4066427 .4912512

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       x |    7508    .3890517    .0056269    .4875676    .3780213    .4000821
       y |    5570    .4066427    .0065823    .4912512    .3937389    .4195465
---------+--------------------------------------------------------------------
combined |   13078    .3965438    .0042777    .4891985    .3881588    .4049288
---------+--------------------------------------------------------------------
    diff |            -.017591      .00865               -.0345462   -.0006358
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  -2.0337
Ho: diff = 0                                     degrees of freedom =    13076

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0210         Pr(|T| > |t|) = 0.0420          Pr(T > t) = 0.9790

. ttesti 7588 .304428 .4601951 5474 .3280965 .469563

Two-sample t test with equal variances
------------------------------------------------------------------------------
         |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       x |    7588     .304428     .005283    .4601951    .2940719    .3147841
       y |    5474    .3280965    .0063466     .469563    .3156546    .3405384
---------+--------------------------------------------------------------------
combined |   13062     .314347    .0040623     .464273    .3063843    .3223096
---------+--------------------------------------------------------------------
    diff |           -.0236685    .0082308                -.039802    -.007535
------------------------------------------------------------------------------
    diff = mean(x) - mean(y)                                      t =  -2.8756
Ho: diff = 0                                     degrees of freedom =    13060

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0020         Pr(|T| > |t|) = 0.0040          Pr(T > t) = 0.9980

. 
. * The means are nonsignificant for the lower two income quartiles
. * The means are significantly different for the upper two income quartiles (two-tailed test)
. 
. * Estimating treatment effect using a multilevel model
. * First model only includes the treatment variable
. 
. logit vote2 treatment, vce(cluster census_tract)

Iteration 0:   log pseudolikelihood = -33223.749  
Iteration 1:   log pseudolikelihood = -33221.235  
Iteration 2:   log pseudolikelihood = -33221.235  

Logistic regression                               Number of obs   =      52324
                                                  Wald chi2(1)    =       4.69
                                                  Prob > chi2     =     0.0304
Log pseudolikelihood = -33221.235                 Pseudo R2       =     0.0001

                         (Std. Err. adjusted for 794 clusters in census_tract)
------------------------------------------------------------------------------
             |               Robust
       vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |   .0421163    .019455     2.16   0.030     .0039851    .0802474
       _cons |  -.7210941   .0222877   -32.35   0.000    -.7647772    -.677411
------------------------------------------------------------------------------

. 
. outreg2 using "ML results.xls", replace ctitle(Model 1) dec(3) addstat("Log Pseudolikelihood", e(ll), "
> Wald Chi-Square", e(chi2))
ML results.xls
dir : seeout

. 
. * Now include individual-level terms
. 
. logit vote2 treatment female age white latino other, vce(cluster census_tract)

Iteration 0:   log pseudolikelihood = -33223.749  
Iteration 1:   log pseudolikelihood = -32889.444  
Iteration 2:   log pseudolikelihood = -32888.116  
Iteration 3:   log pseudolikelihood = -32888.116  

Logistic regression                               Number of obs   =      52324
                                                  Wald chi2(6)    =     393.16
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -32888.116                 Pseudo R2       =     0.0101

                         (Std. Err. adjusted for 794 clusters in census_tract)
------------------------------------------------------------------------------
             |               Robust
       vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |    .042833   .0196092     2.18   0.029     .0043996    .0812664
      female |  -.0958543   .0185646    -5.16   0.000    -.1322402   -.0594684
         age |   .0010148   .0036872     0.28   0.783    -.0062121    .0082416
       white |   .4218609   .0350459    12.04   0.000     .3531722    .4905496
      latino |     .73748   .0400974    18.39   0.000     .6588905    .8160694
       other |   .5226905   .0419872    12.45   0.000      .440397    .6049839
       _cons |  -1.074524   .0989899   -10.85   0.000     -1.26854   -.8805071
------------------------------------------------------------------------------

. 
. outreg2 using "ML results.xls", append ctitle(Model 2) dec(3) addstat("Log Pseudolikelihood", e(ll), "W
> ald Chi-Square", e(chi2))
ML results.xls
dir : seeout

. 
. * Now add in census tract-level terms
. 
. melogit vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino pc
> t_hs_diploma pct_some_college pct_college_grad pct_pro_degree quart_inc|| census_tract:, vce(cluster ce
> nsus_tract)

Fitting fixed-effects model:

Iteration 0:   log likelihood = -32532.243  
Iteration 1:   log likelihood = -32479.662  
Iteration 2:   log likelihood = -32479.633  
Iteration 3:   log likelihood = -32479.633  

Refining starting values:

Grid node 0:   log likelihood = -32722.263

Fitting full model:

Iteration 0:   log pseudolikelihood = -32722.263  (not concave)
Iteration 1:   log pseudolikelihood = -32513.506  (not concave)
Iteration 2:   log pseudolikelihood =  -32332.46  
Iteration 3:   log pseudolikelihood = -32322.488  
Iteration 4:   log pseudolikelihood = -32322.301  
Iteration 5:   log pseudolikelihood = -32322.301  

Mixed-effects logistic regression               Number of obs      =     52324
Group variable:    census_tract                 Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406

Integration method: mvaghermite                 Integration points =         7

                                                Wald chi2(16)      =    864.16
Log pseudolikelihood = -32322.301               Prob > chi2        =    0.0000
                             (Std. Err. adjusted for 794 clusters in census_tract)
----------------------------------------------------------------------------------
                 |               Robust
           vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .0426017   .0199009     2.14   0.032     .0035967    .0816067
          female |  -.0744512   .0189622    -3.93   0.000    -.1116163    -.037286
             age |   -.000762   .0033736    -0.23   0.821    -.0073742    .0058502
           white |   .0669747    .035768     1.87   0.061    -.0031293    .1370787
          latino |   .0757816   .0476199     1.59   0.112    -.0175517    .1691149
           other |    .096726   .0453246     2.13   0.033     .0078913    .1855607
         log_pop |  -.1001984    .031154    -3.22   0.001    -.1612591   -.0391377
         med_age |    .016917   .0031853     5.31   0.000     .0106739    .0231601
       pct_white |   .0003001   .0014792     0.20   0.839     -.002599    .0031992
       pct_black |  -.0069087   .0014817    -4.66   0.000    -.0098128   -.0040047
      pct_latino |   .0072081   .0013095     5.50   0.000     .0046416    .0097746
  pct_hs_diploma |   .0031566   .0029352     1.08   0.282    -.0025964    .0089096
pct_some_college |   .0120124   .0025457     4.72   0.000      .007023    .0170019
pct_college_grad |  -.0003038   .0032479    -0.09   0.925    -.0066696     .006062
  pct_pro_degree |   .0062394   .0035017     1.78   0.075    -.0006238    .0131027
       quart_inc |  -.0084169   .0273007    -0.31   0.758    -.0619252    .0450914
           _cons |  -.8421148   .3751568    -2.24   0.025    -1.577409   -.1068211
-----------------+----------------------------------------------------------------
census_tract     |
       var(_cons)|   .0766806   .0076285                      .0630963    .0931895
----------------------------------------------------------------------------------

. 
. outreg2 using "ML results.xls", append ctitle(Model 3) dec(3) addstat("Log Pseudolikelihood", e(ll), "W
> ald Chi-Square", e(chi2))
ML results.xls
dir : seeout

. 
. * interact treatment with income groups
. 
. melogit vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino pc
> t_hs_diploma pct_some_college pct_college_grad pct_pro_degree treatment##quart_inc|| census_tract:, vce
> (cluster census_tract)
note: 1.treatment omitted because of collinearity

Fitting fixed-effects model:

Iteration 0:   log likelihood = -32460.312  
Iteration 1:   log likelihood =  -32409.75  
Iteration 2:   log likelihood = -32409.719  
Iteration 3:   log likelihood = -32409.719  

Refining starting values:

Grid node 0:   log likelihood = -32715.649

Fitting full model:

Iteration 0:   log pseudolikelihood = -32715.649  (not concave)
Iteration 1:   log pseudolikelihood = -32506.284  (not concave)
Iteration 2:   log pseudolikelihood = -32316.846  
Iteration 3:   log pseudolikelihood = -32304.467  
Iteration 4:   log pseudolikelihood = -32293.994  
Iteration 5:   log pseudolikelihood = -32293.806  
Iteration 6:   log pseudolikelihood = -32293.805  

Mixed-effects logistic regression               Number of obs      =     52324
Group variable:    census_tract                 Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406

Integration method: mvaghermite                 Integration points =         7

                                                Wald chi2(21)      =    990.88
Log pseudolikelihood = -32293.805               Prob > chi2        =    0.0000
                                (Std. Err. adjusted for 794 clusters in census_tract)
-------------------------------------------------------------------------------------
                    |               Robust
              vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.0166083   .0384601    -0.43   0.666    -.0919887    .0587722
             female |  -.0749691   .0189542    -3.96   0.000    -.1121187   -.0378195
                age |  -.0007895   .0033641    -0.23   0.814    -.0073829    .0058039
              white |   .0658996   .0355566     1.85   0.064    -.0037901    .1355892
             latino |   .0788277   .0475241     1.66   0.097    -.0143179    .1719733
              other |   .0955677   .0451512     2.12   0.034      .007073    .1840624
            log_pop |  -.1050753    .029846    -3.52   0.000    -.1635723   -.0465783
            med_age |   .0137122   .0031346     4.37   0.000     .0075685     .019856
          pct_white |   .0014406   .0014676     0.98   0.326    -.0014359    .0043171
          pct_black |  -.0053612   .0014564    -3.68   0.000    -.0082157   -.0025068
         pct_latino |   .0064362   .0013743     4.68   0.000     .0037427    .0091298
     pct_hs_diploma |   .0013262   .0029105     0.46   0.649    -.0043783    .0070308
   pct_some_college |   .0060109   .0026186     2.30   0.022     .0008787    .0111432
   pct_college_grad |  -.0001117   .0031979    -0.03   0.972    -.0063794     .006156
     pct_pro_degree |   .0049629   .0031017     1.60   0.110    -.0011164    .0110422
        1.treatment |          0  (omitted)
                    |
          quart_inc |
                 2  |   .0496784   .0541365     0.92   0.359    -.0564272    .1557841
                 3  |   .1419932   .0672788     2.11   0.035     .0101291    .2738573
                 4  |  -.2168205   .0893516    -2.43   0.015    -.3919464   -.0416946
                    |
treatment#quart_inc |
               1 2  |   .0057742   .0541321     0.11   0.915    -.1003228    .1118712
               1 3  |   .0944627    .053319     1.77   0.076    -.0100407     .198966
               1 4  |   .1253947   .0584839     2.14   0.032     .0107684    .2400211
                    |
              _cons |  -.6019031   .3738997    -1.61   0.107    -1.334733    .1309269
--------------------+----------------------------------------------------------------
census_tract        |
          var(_cons)|   .0649393   .0069914                      .0525855    .0801953
-------------------------------------------------------------------------------------

. 
. outreg2 using "ML results.xls", append ctitle(Model 4) dec(3) addstat("Log Pseudolikelihood", e(ll), "W
> ald Chi-Square", e(chi2))
ML results.xls
dir : seeout

. 
. * Estimate predicted probabilities
. 
. set scheme s2mono

. logit vote2 i.treatment female age i.race, vce(cluster census_tract)

Iteration 0:   log pseudolikelihood = -33223.749  
Iteration 1:   log pseudolikelihood = -32889.444  
Iteration 2:   log pseudolikelihood = -32888.116  
Iteration 3:   log pseudolikelihood = -32888.116  

Logistic regression                               Number of obs   =      52324
                                                  Wald chi2(6)    =     393.16
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -32888.116                 Pseudo R2       =     0.0101

                         (Std. Err. adjusted for 794 clusters in census_tract)
------------------------------------------------------------------------------
             |               Robust
       vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
 1.treatment |    .042833   .0196092     2.18   0.029     .0043996    .0812664
      female |  -.0958543   .0185646    -5.16   0.000    -.1322402   -.0594684
         age |   .0010148   .0036872     0.28   0.783    -.0062121    .0082416
             |
        race |
      Black  |  -.4218609   .0350459   -12.04   0.000    -.4905496   -.3531722
     Latino  |   .3156191   .0367159     8.60   0.000     .2436573    .3875809
      Other  |   .1008296   .0315311     3.20   0.001     .0390298    .1626294
             |
       _cons |  -.6526629   .0950721    -6.86   0.000    -.8390009   -.4663249
------------------------------------------------------------------------------

. margins treatment, atmeans

Adjusted predictions                              Number of obs   =      52324
Model VCE    : Robust

Expression   : Pr(vote2), predict()
at           : 0.treatment     =    .5761219 (mean)
               1.treatment     =    .4238781 (mean)
               female          =    .5951189 (mean)
               age             =    25.45616 (mean)
               1.race          =    .4903486 (mean)
               2.race          =    .2463879 (mean)
               3.race          =    .1372793 (mean)
               4.race          =    .1259843 (mean)

------------------------------------------------------------------------------
             |            Delta-method
             |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |
          0  |    .324803   .0044081    73.68   0.000     .3161632    .3334428
          1  |   .3342661    .004351    76.83   0.000     .3257383    .3427939
------------------------------------------------------------------------------

. marginsplot

  Variables that uniquely identify margins: treatment

. 
. graph save Graph "/usr/local/stata/projects/Chicago votes experiment/Chicago turnout predicted probabil
> ities.gph", replace
(file /usr/local/stata/projects/Chicago votes experiment/Chicago turnout predicted probabilities.gph save
> d)

. 
. * interact treatment with racial groups (not reported in paper)
. 
. melogit vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino pc
> t_hs_diploma pct_some_college pct_college_grad pct_pro_degree quart_inc treatment##i.race|| census_trac
> t:, vce(cluster census_tract)
note: 1.treatment omitted because of collinearity
note: 2.race omitted because of collinearity
note: 3.race omitted because of collinearity
note: 4.race omitted because of collinearity

Fitting fixed-effects model:

Iteration 0:   log likelihood = -32530.926  
Iteration 1:   log likelihood = -32478.309  
Iteration 2:   log likelihood =  -32478.28  
Iteration 3:   log likelihood =  -32478.28  

Refining starting values:

Grid node 0:   log likelihood =   -32721.1

Fitting full model:

Iteration 0:   log pseudolikelihood =   -32721.1  (not concave)
Iteration 1:   log pseudolikelihood =  -32512.35  (not concave)
Iteration 2:   log pseudolikelihood = -32331.238  
Iteration 3:   log pseudolikelihood = -32321.176  
Iteration 4:   log pseudolikelihood = -32320.994  
Iteration 5:   log pseudolikelihood = -32320.993  

Mixed-effects logistic regression               Number of obs      =     52324
Group variable:    census_tract                 Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406

Integration method: mvaghermite                 Integration points =         7

                                                Wald chi2(19)      =    868.21
Log pseudolikelihood = -32320.993               Prob > chi2        =    0.0000
                             (Std. Err. adjusted for 794 clusters in census_tract)
----------------------------------------------------------------------------------
                 |               Robust
           vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .0536816   .0293739     1.83   0.068    -.0038902    .1112533
          female |  -.0747626   .0189448    -3.95   0.000    -.1118938   -.0376315
             age |  -.0007393   .0033738    -0.22   0.827    -.0073517    .0058732
           white |   .0519295   .0414367     1.25   0.210     -.029285    .1331441
          latino |   .0453486   .0537401     0.84   0.399      -.05998    .1506772
           other |   .1110218     .05354     2.07   0.038     .0060852    .2159583
         log_pop |  -.1002944   .0311697    -3.22   0.001    -.1613859    -.039203
         med_age |   .0169391   .0031818     5.32   0.000     .0107028    .0231753
       pct_white |   .0003034   .0014789     0.21   0.837    -.0025952    .0032019
       pct_black |  -.0069071    .001481    -4.66   0.000    -.0098098   -.0040045
      pct_latino |   .0072055    .001309     5.50   0.000       .00464     .009771
  pct_hs_diploma |   .0031571   .0029352     1.08   0.282    -.0025958      .00891
pct_some_college |   .0119953   .0025439     4.72   0.000     .0070094    .0169811
pct_college_grad |  -.0003125   .0032465    -0.10   0.923    -.0066755    .0060505
  pct_pro_degree |   .0062131   .0035031     1.77   0.076    -.0006527     .013079
       quart_inc |  -.0081834   .0273032    -0.30   0.764    -.0616967    .0453299
     1.treatment |          0  (omitted)
                 |
            race |
          Black  |          0  (omitted)
         Latino  |          0  (omitted)
          Other  |          0  (omitted)
                 |
  treatment#race |
        1#Black  |   -.035014   .0475246    -0.74   0.461    -.1281605    .0581325
       1#Latino  |   .0364646   .0597843     0.61   0.542    -.0807104    .1536396
        1#Other  |  -.0690542   .0614682    -1.12   0.261    -.1895297    .0514212
                 |
           _cons |  -.8319136    .377208    -2.21   0.027    -1.571228   -.0925994
-----------------+----------------------------------------------------------------
census_tract     |
       var(_cons)|   .0766383   .0076246                      .0630611    .0931388
----------------------------------------------------------------------------------

. 
. outreg2 using "unreported results.xls", replace ctitle(Race) dec(3) addstat("Log Pseudolikelihood", e(l
> l), "Wald Chi-Square", e(chi2))
unreported results.xls
dir : seeout

. 
. * interact treatment with college educated population (not reported in paper)
. 
. melogit vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino qu
> art_inc treatment##quart_college|| census_tract:, vce(cluster census_tract)
note: 1.treatment omitted because of collinearity

Fitting fixed-effects model:

Iteration 0:   log likelihood = -32479.088  
Iteration 1:   log likelihood = -32428.546  
Iteration 2:   log likelihood = -32428.518  
Iteration 3:   log likelihood = -32428.518  

Refining starting values:

Grid node 0:   log likelihood = -32718.253

Fitting full model:

Iteration 0:   log pseudolikelihood = -32718.253  (not concave)
Iteration 1:   log pseudolikelihood = -32509.003  (not concave)
Iteration 2:   log pseudolikelihood = -32321.104  
Iteration 3:   log pseudolikelihood = -32304.346  
Iteration 4:   log pseudolikelihood = -32300.698  
Iteration 5:   log pseudolikelihood = -32300.681  
Iteration 6:   log pseudolikelihood = -32300.681  

Mixed-effects logistic regression               Number of obs      =     52324
Group variable:    census_tract                 Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406

Integration method: mvaghermite                 Integration points =         7

                                                Wald chi2(18)      =    903.98
Log pseudolikelihood = -32300.681               Prob > chi2        =    0.0000
                                    (Std. Err. adjusted for 794 clusters in census_tract)
-----------------------------------------------------------------------------------------
                        |               Robust
                  vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------+----------------------------------------------------------------
              treatment |   .0096158   .0384096     0.25   0.802    -.0656657    .0848972
                 female |  -.0744092   .0189234    -3.93   0.000    -.1114984   -.0373201
                    age |  -.0015714   .0033472    -0.47   0.639    -.0081318    .0049891
                  white |   .0660772   .0357462     1.85   0.065     -.003984    .1361385
                 latino |   .0846294   .0475916     1.78   0.075    -.0086484    .1779072
                  other |   .0948435   .0453254     2.09   0.036     .0060075    .1836796
                log_pop |  -.0904069   .0296882    -3.05   0.002    -.1485947    -.032219
                med_age |   .0137624   .0031258     4.40   0.000      .007636    .0198888
              pct_white |   .0007666   .0013888     0.55   0.581    -.0019555    .0034887
              pct_black |   -.004677   .0013985    -3.34   0.001    -.0074179   -.0019361
             pct_latino |   .0067884   .0010905     6.22   0.000     .0046511    .0089258
              quart_inc |   .0091745   .0239922     0.38   0.702    -.0378492    .0561983
            1.treatment |          0  (omitted)
                        |
          quart_college |
                     2  |   .2031992   .0454372     4.47   0.000     .1141439    .2922544
                     3  |    .254732   .0633614     4.02   0.000      .130546     .378918
                     4  |  -.0460748   .0997548    -0.46   0.644    -.2415907    .1494411
                        |
treatment#quart_college |
                   1 2  |    .005129    .052198     0.10   0.922    -.0971773    .1074352
                   1 3  |   .0347873   .0552441     0.63   0.529    -.0734891    .1430636
                   1 4  |   .0872384   .0584191     1.49   0.135     -.027261    .2017378
                        |
                  _cons |  -.5932315   .3194686    -1.86   0.063    -1.219378    .0329155
------------------------+----------------------------------------------------------------
census_tract            |
              var(_cons)|   .0682965   .0075854                      .0549363    .0849058
-----------------------------------------------------------------------------------------

. 
. outreg2 using "unreported results.xls", append ctitle(College) dec(3) addstat("Log Pseudolikelihood", e
> (ll), "Wald Chi-Square", e(chi2))
unreported results.xls
dir : seeout

. 
. * replicate logit and multilevel models using OLS (not reported in paper)
. 
. regress vote2 treatment, vce(cluster census_tract)

Linear regression                                      Number of obs =   52324
                                                       F(  1,   793) =    4.69
                                                       Prob > F      =  0.0307
                                                       R-squared     =  0.0001
                                                       Root MSE      =   .4706

                         (Std. Err. adjusted for 794 clusters in census_tract)
------------------------------------------------------------------------------
             |               Robust
       vote2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |   .0093374   .0043121     2.17   0.031      .000873    .0178018
       _cons |   .3271521   .0049061    66.68   0.000     .3175216    .3367826
------------------------------------------------------------------------------

. 
. outreg2 using "unreported OLS results.xls", replace ctitle(Model 1) dec(3)
unreported OLS results.xls
dir : seeout

. 
. * Now include individual-level terms
. 
. regress vote2 treatment female age white latino other, vce(cluster census_tract)

Linear regression                                      Number of obs =   52324
                                                       F(  6,   793) =   68.79
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0126
                                                       Root MSE      =  .46766

                         (Std. Err. adjusted for 794 clusters in census_tract)
------------------------------------------------------------------------------
             |               Robust
       vote2 |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   treatment |   .0093618   .0042935     2.18   0.030     .0009338    .0177898
      female |  -.0210775   .0040963    -5.15   0.000    -.0291183   -.0130366
         age |   .0002274   .0008093     0.28   0.779    -.0013612     .001816
       white |   .0871834   .0071866    12.13   0.000     .0730764    .1012904
      latino |   .1610031   .0087857    18.33   0.000     .1437572    .1782491
       other |   .1101473   .0089894    12.25   0.000     .0925015    .1277931
       _cons |   .2551668    .021433    11.91   0.000     .2130947    .2972389
------------------------------------------------------------------------------

. 
. outreg2 using "unreported OLS results.xls", append ctitle(Model 2) dec(3)
unreported OLS results.xls
dir : seeout

. 
. * Now add in census tract-level terms
. 
. mixed vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino pct_
> hs_diploma pct_some_college pct_college_grad pct_pro_degree quart_inc|| census_tract:, vce(cluster cens
> us_tract)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood = -33898.465  
Iteration 1:   log pseudolikelihood = -33898.465  

Computing standard errors:

Mixed-effects regression                        Number of obs      =     52324
Group variable: census_tract                    Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406


                                                Wald chi2(16)      =    902.16
Log pseudolikelihood = -33898.465               Prob > chi2        =    0.0000

                             (Std. Err. adjusted for 794 clusters in census_tract)
----------------------------------------------------------------------------------
                 |               Robust
           vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
       treatment |   .0090054   .0042191     2.13   0.033     .0007361    .0172748
          female |  -.0158305   .0040548    -3.90   0.000    -.0237777   -.0078833
             age |  -.0001365   .0007137    -0.19   0.848    -.0015353    .0012624
           white |   .0131146   .0070159     1.87   0.062    -.0006363    .0268654
          latino |   .0151561   .0101992     1.49   0.137    -.0048338    .0351461
           other |   .0196274   .0093335     2.10   0.035     .0013341    .0379208
         log_pop |  -.0220417   .0066687    -3.31   0.001    -.0351122   -.0089712
         med_age |    .003351    .000649     5.16   0.000      .002079     .004623
       pct_white |   .0001395   .0003388     0.41   0.681    -.0005246    .0008036
       pct_black |  -.0014607   .0003377    -4.33   0.000    -.0021226   -.0007988
      pct_latino |   .0014976   .0002913     5.14   0.000     .0009266    .0020686
  pct_hs_diploma |   .0007139   .0006281     1.14   0.256    -.0005172    .0019449
pct_some_college |   .0024775   .0005375     4.61   0.000      .001424    .0035309
pct_college_grad |  -.0003052   .0007043    -0.43   0.665    -.0016856    .0010752
  pct_pro_degree |   .0012364   .0007641     1.62   0.106    -.0002612     .002734
       quart_inc |  -.0016126   .0058886    -0.27   0.784    -.0131541    .0099289
           _cons |    .327616   .0805086     4.07   0.000     .1698221    .4854099
----------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
census_tract: Identity       |
                  var(_cons) |   .0036268   .0003543      .0029948    .0043922
-----------------------------+------------------------------------------------
               var(Residual) |   .2116249   .0014891      .2087264    .2145638
------------------------------------------------------------------------------

. 
. outreg2 using "unreported OLS results.xls", append ctitle(Model 3) dec(3) addstat("Log Pseudolikelihood
> ", e(ll), "Wald Chi-Square", e(chi2))
unreported OLS results.xls
dir : seeout

. 
. * interact treatment with income groups
. 
. mixed vote2 treatment female age white latino other log_pop med_age pct_white pct_black pct_latino pct_
> hs_diploma pct_some_college pct_college_grad pct_pro_degree treatment##quart_inc|| census_tract:, vce(c
> luster census_tract)
note: 1.treatment omitted because of collinearity

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log pseudolikelihood =   -33868.9  
Iteration 1:   log pseudolikelihood = -33868.898  

Computing standard errors:

Mixed-effects regression                        Number of obs      =     52324
Group variable: census_tract                    Number of groups   =       794

                                                Obs per group: min =         3
                                                               avg =      65.9
                                                               max =       406


                                                Wald chi2(21)      =   1037.02
Log pseudolikelihood = -33868.898               Prob > chi2        =    0.0000

                                (Std. Err. adjusted for 794 clusters in census_tract)
-------------------------------------------------------------------------------------
                    |               Robust
              vote2 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------------+----------------------------------------------------------------
          treatment |  -.0031026   .0071776    -0.43   0.666    -.0171704    .0109653
             female |  -.0159471   .0040533    -3.93   0.000    -.0238915   -.0080026
                age |  -.0001379   .0007121    -0.19   0.846    -.0015336    .0012578
              white |   .0128545   .0069778     1.84   0.065    -.0008216    .0265307
             latino |   .0158491   .0102011     1.55   0.120    -.0041447     .035843
              other |   .0193511   .0093063     2.08   0.038     .0011111    .0375911
            log_pop |  -.0228686   .0063868    -3.58   0.000    -.0353865   -.0103507
            med_age |   .0026682   .0006387     4.18   0.000     .0014164      .00392
          pct_white |   .0003948   .0003366     1.17   0.241    -.0002649    .0010546
          pct_black |  -.0011193   .0003316    -3.38   0.001    -.0017691   -.0004694
         pct_latino |   .0013398   .0003053     4.39   0.000     .0007415    .0019382
     pct_hs_diploma |   .0003256   .0006209     0.52   0.600    -.0008913    .0015424
   pct_some_college |   .0012009   .0005501     2.18   0.029     .0001226    .0022791
   pct_college_grad |  -.0002334    .000691    -0.34   0.736    -.0015877    .0011208
     pct_pro_degree |    .000979   .0006772     1.45   0.148    -.0003482    .0023062
        1.treatment |          0  (omitted)
                    |
          quart_inc |
                 2  |   .0100719   .0113335     0.89   0.374    -.0121414    .0322852
                 3  |   .0310865   .0144741     2.15   0.032     .0027178    .0594553
                 4  |  -.0474965   .0193275    -2.46   0.014    -.0853776   -.0096154
                    |
treatment#quart_inc |
               1 2  |   .0007073   .0109149     0.06   0.948    -.0206856    .0221002
               1 3  |   .0212089     .01121     1.89   0.058    -.0007623      .04318
               1 4  |   .0261818   .0117725     2.22   0.026     .0031081    .0492554
                    |
              _cons |    .375248    .080241     4.68   0.000     .2179787    .5325174
-------------------------------------------------------------------------------------

------------------------------------------------------------------------------
                             |               Robust           
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
census_tract: Identity       |
                  var(_cons) |   .0030641   .0003294       .002482    .0037828
-----------------------------+------------------------------------------------
               var(Residual) |   .2116395   .0014906      .2087382    .2145812
------------------------------------------------------------------------------

. 
. outreg2 using "unreported OLS results.xls", append ctitle(Model 4) dec(3) addstat("Log Pseudolikelihood
> ", e(ll), "Wald Chi-Square", e(chi2))
unreported OLS results.xls
dir : seeout

. 
. log close
      name:  <unnamed>
       log:  /usr/local/stata/projects/Chicago votes experiment/do and log files/analysis-chicago-experim
> ent-4.log
  log type:  text
 closed on:  18 May 2017, 12:50:40
---------------------------------------------------------------------------------------------------------
